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Enregistrement W7061913186

Short-term carbon dioxide and nitrous oxide flux following tillage of the clay soil in the Red River Valley in Southern Manitoba

2008· dissertation· en· W7061913186 sur OpenAlexfundaboutno aff

Notice bibliographique

RevueMspace (University of Manitoba) · 2008
Typedissertation
Langueen
DomaineEngineering
ThématiqueSuperconducting Materials and Applications
Établissements canadiensnon disponible
Organismes subventionnairesNatural Sciences and Engineering Research Council of CanadaUniversity of Manitoba
Mots-clésTillageTopsoilConventional tillageSoil waterSoil textureCrop residueVegetation (pathology)
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

There has been resurgence in the interest of conservation tillage as a way to sequester carbon from the atmosphere, to help improve soil quality, and as a means to mitigate the increasing concentration of greenhouse gases (GHGs) in the atmosphere. However, the term conservation tillage is qualitative and quite ambiguous, and refers to a wide range of tillage practices. This makes the interpretation of information gathered from different tillage systems difficult. There currently exists a need for the quantification of soil surface properties following different tillage methods because surface properties are closely linked to soil surface processes. Previous research has focused on the long-term impacts of tillage systems and their effects on soil biological processes and properties, such as soil microbial populations and activity, soil organic matter fractions and their role in the production and emission of greenhouse gases. However, the more immediate impacts of tillage on soil physical processes and properties and their role in the production and emission of GHGs are not well understood and are often overlooked. The first objective of this research addressed the need for better quantification of soil physical properties after tillage practices. This research demonstrated the use of a laser profiling system and digital imagery and image analysis software in measuring soil micro-relief and crop residue cover. Furthermore, comparisons of geostatisitical and univariate procedures of quantifying surface roughness were also investigated. There was a definite advantage in using a geostatistical approach to characterize soil topography as the indices they provide give insight into the characterisitcs of the surface roughness. Soil disturbance and the addition of corn residue were both found to be significant factors affecting the surface roughness, crop residue cover, exposed surface area, and near-surface porosity. The second objective of this research focused on the quantification and characterization of the short-term effects of soil disturbance as a result of tillage on the carbon dioxide (CO2) and nitrous oxide (N2O) flux from the clay soils of the Red River Valley, Manitoba. The short-term CO2 flux (up to 5 days) following a soil disturbance event was characterized by an immediate increase in the CO2 flux following the soil disturbance event that quickly dissipated within the first 24 hours. Both the addition of residue and soil disturbance were found to be significant factors in the cumulative CO2 loss over the 5-day observation period. However, the incorporation of the residue through the action of soil disturbance was found to be a more important factor than soil disturbance or the addition of residue alone. The effects of residue and soil disturbance on the N2O flux were highly variable. However, there was some indication that the N2O flux under soil conditions may have a response to soil disturbance similar to that of CO2. The third objective is a combination in the previous two objectives and deals with the need to better understand the underlying physical mechanisms that control the CO2 and N2O flux. This was accomplished by combining the detailed information on the changes in surface properties and the CO2 and N2O fluxes that occur due to soil disturbance. Generally, the soil disturbance treatments that resulted in a rougher surface...

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,594
Score d'incertitude au seuil0,994

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,013
Tête enseignante GPT0,189
Écart entre enseignants0,176 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2008
Routes d'admission2
Résumé présentoui

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